In order to detect any type of outlier, we propose an approach based on the Shannon Entropy [1]} to detect the unknown class. The classifiers used in this work output the probabilities for each class using the softmax function. Basically, when a classifier is in doubt about a new sample, and it may be an outlier, it assigns a value for the probability of different classes. On the other hand, when the classifier is certain about its decision, it assigns a high probability for only one class. As a result, the entropy for the first case is higher than that one for the last case. This approach exploits this point.
